Lead AI Engineer

Job Title: Lead AI Engineer

Location:
Raleigh NC/ Foster City, CA

Work Model:
Hybrid and Remote

About the Role

We are seeking a hands-on
Lead AI Engineer
to lead the design, development, and deployment of enterprise-grade Artificial Intelligence and Generative AI solutions on AWS and modern cloud platforms. This role goes beyond experimentation, you will architect secure, scalable, cost-efficient, and production-ready AI systems leveraging
Large Language Models (LLMs)
,
Retrieval-Augmented Generation (RAG)
,
AI Agents
,
semantic search
, and
foundation models
. You will bridge the gap between cutting-edge AI research and practical business applications while ensuring governance, security, performance, and compliance. You will collaborate closely with Data Science, Engineering, Product, Security, and MLOps teams to deliver
next-generation
AI-powered applications
, including intelligent assistants, document intelligence platforms, enterprise search systems, and autonomous AI workflows.

Key Responsibilities

AI & Solution Architecture

• Architect and implement enterprise-scale AI and Generative AI solutions using LLMs such as GPT, Claude, Llama, Mixtral, and other foundation models.
• Design end-to-end AI system architectures including data pipelines, model orchestration, API integrations, vector search layers, and application services.
• Design and deploy Retrieval-Augmented Generation (RAG) pipelines for document intelligence, enterprise search, knowledge management, and conversational AI applications.
• Build AI-powered assistants, chatbots, autonomous agents, and intelligent workflow automation solutions.
• Develop semantic search and embedding pipelines using vector databases and retrieval frameworks.

Model Strategy & Optimization

• Evaluate, benchmark, and select appropriate foundation models based on business requirements, performance, cost, and scalability.
• Determine optimal approaches including prompt engineering, RAG, fine-tuning, LoRA, QLoRA, and PEFT techniques.
• Optimize prompts, inference strategies, token consumption, latency, throughput, and overall system performance.
• Implement hallucination mitigation techniques through grounding, validation layers, guardrails, and prompt constraints.

Security, Governance & Responsible AI

• Design secure AI architectures with strong emphasis on data privacy, compliance, and enterprise security.
• Implement guardrails to prevent prompt injection, data leakage, misuse, and model vulnerabilities.
• Establish Responsible AI practices, bias mitigation strategies, explainability frameworks, and governance standards.
• Ensure compliance with enterprise and regulatory requirements including HIPAA, GDPR, and industry-specific standards where applicable.

Cloud & Platform Engineering

• Design scalable AI solutions on AWS leveraging services such as Bedrock, SageMaker, Lambda, API Gateway, S3, DynamoDB, IAM, KMS, VPC, and CloudTrail.
• Architect containerized and cloud-native AI platforms using Docker and Kubernetes.
• Design multi-tenant AI architectures and enterprise-grade deployment models.
• Optimize infrastructure for performance, availability, scalability, and cost efficiency.

MLOps / LLMOps

• Establish CI/CD pipelines for AI and ML systems.
• Implement model versioning, monitoring, observability, drift detection, rollback strategies, and automated deployment workflows.
• Define evaluation frameworks, logging standards, performance metrics, and operational monitoring practices.
• Collaborate with MLOps teams to productionize and operationalize AI solutions at scale.

Collaboration & Leadership

• Partner with Engineering, Data Science, Product, Security, and Business teams to translate complex business requirements into scalable AI solutions.
• Provide technical leadership and architectural guidance across AI initiatives.
• Mentor engineers and promote AI best practices, innovation, and engineering excellence.
• Align AI capabilities with measurable business outcomes and strategic objectives.

Required Skills & Experience

Generative AI & Large Language Models

• Strong understanding of LLM architectures, inference mechanisms, and foundation models.
• Hands-on experience designing and deploying production-grade RAG systems.
• Experience with prompt engineering, prompt optimization, and contextual retrieval.
• Knowledge of LoRA, QLoRA, PEFT, and model fine-tuning techniques.
• Experience implementing hallucination reduction and factuality improvement strategies.
• Familiarity with AI Agents, autonomous workflows, and agent orchestration frameworks.

Embeddings & Retrieval

• Semantic embedding models (OpenAI, Sentence-BERT, Hugging Face, etc.).
• Document chunking strategies and metadata management.
• Vector similarity search techniques including cosine similarity and dot-product search.
• Hands-on experience with vector databases such as Pinecone, FAISS, OpenSearch, Milvus, Qdrant, or Chroma.

AI Frameworks & Technologies

• LangChain
• LlamaIndex
• Hugging Face
• PyTorch
• TensorFlow
• FastAPI
• Haystack or equivalent AI frameworks

Cloud & Infrastructure

• AWS (Bedrock, SageMaker, Lambda, API Gateway, S3, DynamoDB, IAM, KMS, VPC, CloudTrail)
• Azure and/or GCP experience is preferred.
• Docker and Kubernetes
• Enterprise cloud architecture and deployment experience

Programming & Software Engineering

• Strong Python programming skills
• Experience with Java or C++ is a plus
• Knowledge of microservices architecture
• Experience with asynchronous processing, caching, and scalable APIs

MLOps & Production AI

• Model monitoring and observability
• CI/CD for AI and ML systems
• Drift detection and performance evaluation
• Model versioning and rollback strategies
• Cost and infrastructure optimization

Qualifications

• 7+ years
  in software/ML engineering, with
  2+ years in GenAI/LLMs
• Proven experience
  designing
  and
  deploying production-grade AI
  applications
  and platforms.